Empirical evaluation of travel survey based on mobile phone sensor data

YANG Fei; GUO Yu-dong; JIN J P; WU Hai-tao

doi:10.19818/j.cnki.1671-1637.2020.01.019

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Article Navigation > Journal of Traffic and Transportation Engineering > 2020 > 20(1): 226-238

YANG Fei, GUO Yu-dong, JIN J P, WU Hai-tao. Empirical evaluation of travel survey based on mobile phone sensor data[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 226-238. doi: 10.19818/j.cnki.1671-1637.2020.01.019

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YANG Fei, GUO Yu-dong, JIN J P, WU Hai-tao. Empirical evaluation of travel survey based on mobile phone sensor data[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 226-238. doi: 10.19818/j.cnki.1671-1637.2020.01.019

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Empirical evaluation of travel survey based on mobile phone sensor data

doi: 10.19818/j.cnki.1671-1637.2020.01.019

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
2.
Department of Civil and Environmental Engineering, Rutgers University, Piscataway 08854, New Jersey, USA

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Author Bio:
YANG Fei(1980-), male, professor, PhD, yangfei_traffic@163.com
Received Date: 2019-08-14
Publish Date: 2020-02-25

Abstract

Abstract

The mobile phone sensor and questionnaire were used to collect the real travel trajectories of college students on campus for 2 weeks. The characteristics of mobile phone sensor data in real travel environment were considered, and the Gaussian filter was applied to pre-process the data. According to the spatio-temporal clustering characteristics of trajectory points, the travel endpoints and travel times were identified by spatio-temporal clustering algorithm. Based on the characteristics of velocity and acceleration of trajectory points, the travel modes were identified by using support vector machine. Comparing the data of mobile phone sensor with the data of questionnaire and screen line, the accuracies of travel feature recognition of mobile phone sensor data were analyzed, and the extraction effect of travel feature was verified. Analysis result shows that the matching degree of travel chain between the mobile phone sensor and questionnaire is 81.66%, which indicates that mobile phone sensor data can effectively record the travel trajectory. When the spatial radius of the core point is 26.92 m, the minimum sample points are 129, and the time constraint is 129 s in the parameters of the spatio-temporal clustering algorithm, the travel endpoint and travel time identification accuracy are 93.02% and 90.84%, respectively, which indicates that the mobile phone sensor can identify the travel endpoint and travel time effectively. The accuracy of travel mode identification is 89.86% when the support vector machine type is classical, the kernel function is radial basis function, the penalty coefficient is 0.797, and the kernel parameter is 2.260, which indicates that mobile phone sensor can effectively identify the travel modes. Therefore, the recognition result of mobile phone sensor data is reasonable, which can support the application of mobile phone sensor data in actual travel survey.
- traffic information,
- mobile phone sensor,
- spatio-temporal clustering algorithm,
- support vector machine,
- travel characteristics,
- questionnaire

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References(31)

References

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Empirical evaluation of travel survey based on mobile phone sensor data

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